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LACNNER: Lexicon-Aware Character Representation for Chinese Nested Named Entity Recognition

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13345))

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Abstract

Named Entity Recognition (NER) is one of fundamental researches in natural language processing. Chinese nested-NER is even more challenging. Recently, studies on NER have generally focused on the extraction of flat structures by sequence annotation strategy while ignoring nested structures. In this paper, we propose a novel model, named LACNNER, that utilizing lexicon-aware character representation for Chinese nested NER. We select the typical character-level framework to overcome error propagation problem caused by incorrect word separation. Considering the situation that Chinese words always contain much richer semantic information than single characters do, it firstly obtains more significant matching words through external lexicon in our LACNNER model, and then generates lexicon-aware character representations that make full use of word-level knowledge for nested named entity. We also evaluate the effectiveness of LACNNER by taking ACE-2005-Zh dataset as a benchmark. The experimental results fully verified the positive effect of incorporating lexicon-aware character-representation in recognition of Chinese nested entity structure.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61732005, No. 61671064) and National Key Research & Development Program (Grant No. 2018YFC0831700).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Zhikun Yang, Shumin Shi, Junyu Tian, En Li & Heyan Huang

  2. Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China

    Shumin Shi & Heyan Huang

Authors
  1. Zhikun Yang
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  2. Shumin Shi
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  3. Junyu Tian
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  4. En Li
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  5. Heyan Huang
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Corresponding author

Correspondence to Shumin Shi .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Yang, Z., Shi, S., Tian, J., Li, E., Huang, H. (2022). LACNNER: Lexicon-Aware Character Representation for Chinese Nested Named Entity Recognition. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-09726-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09725-6

  • Online ISBN: 978-3-031-09726-3

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